Paper Info
Title
Process variation-aware workload partitioning algorithms for GPUs supporting spatial-multitasking
Abstract
High-level programming languages have transformed graphics processing units (GPUs) from domain-restricted devices into powerful compute platforms. Yet many \"general-purpose GPU\" (GPGPU) applications fail to fully utilize the GPU resources. Executing multiple applications simultaneously on different regions of the GPU (spatial multitasking) thus improves system performance. However, within-die process variations lead to significantly different maximum operating frequencies (Fmax) of the streaming multiprocessors (SMs) within a GPU. As the chip size and number of SMs per chip increase, the frequency variation is also expected to increase, exacerbating the problem. The increased number of SMs also provides a unique opportunity: we can allocate resources to concurrently-executing applications based on how those applications are affected by the different available Fmax values. In this paper, we study the effects of per-SM clocking on spatial multitasking-capable GPUs. We demonstrate two factors that affect the performance of simultaneously-running applications: (i) the SM partitioning algorithm that decides how many resources to assign to each application, and (ii) the assignment of SMs to applications based on the operating frequencies of those SMs and the applications characteristics. Our experimental results show that spatial multitasking that partitions SMs based on application characteristics, when combined with per-SM clocking, can greatly improve application performance by up to 46% on average compared to cooperative multitasking with global clocking.
Year
DOI
Venue
2014
10.7873/DATE.2014.189
DATE
Keywords
Field
DocType
general-purpose gpu,partitions sms,cooperative multitasking,application performance,gpu resource,process variation-aware workload,concurrently-executing application,per-sm clocking,applications characteristic,spatial multitasking,application characteristic,high level programming languages,high level languages,frequency control,multitasking,multiprogramming,kernel
Graphics,Computer science,Workload,Parallel computing,Algorithm,Chip,Real-time computing,High-level programming language,Process variation,General-purpose computing on graphics processing units,Computer multitasking,Human multitasking
Conference
ISSN
Citations 
PageRank 
1530-1591
10
0.47
References 
Authors
9
7
Name
Order
Citations
PageRank
Paula Aguilera1423.67
Jungseob Lee216211.44
Amin Farmahini-Farahani31506.63
Katherine Morrow41445.33
Michael Schulte5543.50
Nam Sung Kim63268225.99
Farmahini-Farahani, A.7100.47